For more than half a century, Moore’s Law has defined progress in computing. Shrinking transistors and packing more of them into chips sustained exponential growth that reshaped economies, science, and defense. Yet as scaling slows, the United States faces a critical question. What comes after Moore’s Law, and how can the nation prepare for it? Quantum computing, neuromorphic systems, and other unconventional paradigms promise breakthroughs that traditional architectures cannot deliver. Erik Hosler, who underscores the urgency of looking beyond conventional scaling, recognizes that preparing for these shifts is central to sustaining U.S. leadership. His perspective reflects a growing consensus that innovation must move from incremental advances to transformative leaps.
This shift demands long-term planning. Emerging paradigms are unlikely to replace today’s chips overnight, but they will define the trajectory of computing in the decades ahead. The Special Competitive Studies Project (SCSP) calls for significant investment in moonshot programs that bridge research, prototyping, and deployment. These investments must build resilience in the innovation ecosystem, ensuring that the United States is not caught unprepared when new paradigms mature.
Why New Paradigms Are Needed
Traditional scaling has provided remarkable benefits, but it now faces physical and economic limits. Transistors cannot shrink indefinitely, and energy efficiency gains are slowing. As a result, simply pushing silicon harder will not sustain exponential growth.
Quantum computing offers one alternative, promising exponential power for problems in cryptography, optimization, and scientific simulation. Neuromorphic computing provides another modeling system after the human brain to achieve energy efficiency for pattern recognition and learning tasks. Other emerging paradigms include photonic and superconducting computing, each with potential advantages for specialized applications.
These technologies are not replacements for silicon in every domain, but they address areas where conventional architectures fail. Preparing for them now ensures the U.S. can lead when they become practical.
The Challenge of Long Timelines
Unlike conventional chips that can be iterated rapidly, emerging paradigms require decades of research. Quantum prototypes exist today, but they are noisy and difficult to scale. Neuromorphic systems remain largely experimental, with only a handful of testbeds deployed. Photonic and superconducting approaches face similar challenges.
The long timelines create a dilemma. Governments and companies must commit resources now without certainty about when or how the technologies will mature. This uncertainty can discourage private investment, which often seeks near-term returns. Public funding, therefore, plays a critical role in sustaining momentum across decades.
Building an Innovation Pipeline
Preparing for new paradigms requires more than scattered research projects. It demands an innovation pipeline that connects basic science, prototyping, and commercialization. Federal programs can provide stable funding for foundational research, while consortia link universities, national labs, and industry. When the technologies are ready, private firms translate discoveries into products.
The pipeline must also include workforce development. Training researchers and engineers in quantum physics, neuromorphic design, and novel materials ensures that the U.S. will have the talent to advance these fields. Without a skilled workforce, even well-funded programs will stall.
Scaling Quantum Systems
One of the clearest challenges in new paradigms is scaling prototypes into useful machines. Qubits, the building blocks of quantum computers, remain fragile and prone to noise. Error correction requires thousands of physical qubits for each usable logical qubit. Erik Hosler stresses, “We need hundreds to thousands of usable qubits with the capability to do billions of sequential operations to really do useful work.” His observation highlights the vast distance between current demonstrations and practical systems.
The same challenge applies to other paradigms. Neuromorphic chips must prove they can scale beyond research prototypes to commercial applications. Photonic systems must demonstrate reliability in real-world environments. Scaling is the bottleneck, and solving it requires breakthroughs in design, fabrication, and error mitigation.
Policy as a Catalyst
The SCSP calls for long-term investment to ensure that the U.S. leads in emerging paradigms. It includes moonshot programs modeled on earlier successes in space and defense. Public–private partnerships can share risks while aligning incentives. By providing stable funding, the government enables research that may not yield results for years but could eventually transform global competition.
Policy must also focus on security. If adversaries achieve breakthroughs first, the consequences for U.S. economic and military power would be profound. Export controls, international cooperation, and standards-setting are therefore as important in new paradigms as they are in traditional semiconductors.
The Role of Industry and Academia
Private firms bring agility and scale, but academia provides exploratory research that drives paradigm shifts. Universities have historically pioneered fields like quantum information theory and neuromorphic architectures. Industry then takes these ideas and builds products when the technologies mature. Partnerships between the two are essential to bridge the gap between curiosity-driven research and real-world application.
National labs also play a key role, providing facilities and expertise that neither universities nor private companies can match. By linking these three pillars, the U.S. can sustain an ecosystem capable of advancing multiple paradigms simultaneously.
Looking Beyond Silicon Valley
Just as advanced semiconductor hubs are spreading beyond California, so too must research into new paradigms. Regional hubs with strengths in physics, materials science, and AI research can become centers of excellence for quantum and neuromorphic development. Linking these hubs through national networks ensures that progress is not siloed.
This distributed approach also strengthens resilience. By spreading research and development across regions, the U.S. reduces the risk of bottlenecks and fosters broader participation in transformative technologies.
Preparing for the Next Era
The future of computing will not be defined by one technology alone. Quantum, neuromorphic, photonic, and other paradigms will coexist with traditional silicon, each serving different purposes. Preparing for this future requires vision, patience, and coordination across government, industry, and academia.
For the United States, the choice is clear. Either invest now in long-term research or risk falling behind when the next paradigm arrives. By treating these emerging approaches as strategic priorities, the U.S. can ensure it remains at the forefront of innovation.
The lesson is straightforward. The end of Moore’s Law is not the end of progress. It is the beginning of a new era, one that demands resilience, foresight, and investment in technologies that will shape the decades to come.